Investigating the Impact of Perceived Micro-Level Neighborhood Characteristics on Housing Prices in Shanghai

Qiwei Song, Yifeng Liu, Waishan Qiu, Ruijun Liu and Meikang Li ()
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Qiwei Song: John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, Toronto, ON M5S 2J5, Canada
Yifeng Liu: School of Architecture, Tsinghua University, Beijing 100084, China
Waishan Qiu: Department of City and Regional Planning, Cornell University, Ithaca, NY 14850, USA
Ruijun Liu: Graduate School of Design, Harvard University, Cambridge, MA 02138, USA
Meikang Li: College of Design and Innovation, Shenzhen Technology University, Shenzhen 518118, China

Land, 2022, vol. 11, issue 11, 1-21

Abstract: It is widely accepted that houses in better-designed neighborhoods are found to enjoy a price premium. Prior studies have mainly examined the impact of macro-level neighborhood attributes (e.g., park accessibility using land use data) on housing prices. More recently, research has investigated the micro-level features using street view imagery (SVI) data, though scholars limited the scope to objective indicators such as the green view index and sky view index. The role of subjectively measured street qualities is less discussed due to the lack of large-scale perception data. To provide better explanations of whether and how the micro-level neighborhood environment affects housing prices, this article introduces a framework to collect designers’ perceptions on five subjective urban design perceptions from pairwise SVI rankings in Shanghai with an online visual survey and further predicted through machine learning (ML) algorithms. We also extracted ten important objective features from the scenes. The predictive power of micro-level neighborhood street perceptions (subjective perceptions and objective features) on housing prices was investigated using the hedonic price model (HPM) through ordinary least squares (OLS) and spatial regression, which considers spatial dependence. The findings prove the significance of the value of perceived qualities of the neighborhoods. It reveals that both objective perceived features and subjective perceptions significantly contribute to housing prices; while the objective features show more collective strengths, individual subjective perceptions have more explanatory power, and we argue that these two measures can complement each other. This study provides an important reference for decision makers when selecting street quality indicators to inform city planning, urban design, and community and housing development plans.

Keywords: street environment; micro level; human perception; housing price; street view imagery; computer vision; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:11:p:2002-:d:967255

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